The Customer Effort Score (CES) is a better predictor of churn than CSAT because customer effort is more directly related to the decision to leave. A satisfied customer may still leave if every interaction takes effort. Customers who are served effortlessly stay longer and are more likely to recommend you. In this article, we answer the most frequently asked questions about CES, its relationship to loyalty and how to concretely lower customer effort in your customer contact operation.
What exactly does CES measure and how is it different from CSAT?
The Customer Effort Score (CES) measures how much effort a customer had to make to get a problem solved or a question answered. Customers typically rate this on a scale of 1 to 7, with a low score representing little effort. CSAT (Customer Satisfaction Score), on the other hand, measures overall satisfaction with a product, service or interaction.
The fundamental difference is in perspective. CSAT asks, “Were you satisfied?” CES asks, “How much effort did it take you?” This sounds like a subtle distinction, but it has major implications for the usefulness of the score. Satisfaction is a feeling that is strongly influenced by expectations, mood and context. Effort is a more concrete judgment of the process itself.
A customer who calls with a complaint and is treated kindly may give a reasonable CSAT. But if that same customer was transferred three times and had to repeat his story twice, the CES is low. Exactly that low CES signals the risk of churn; the high CSAT does not.
Why does high customer satisfaction not predict loyalty?
High customer satisfaction does not predict loyalty because satisfaction is a snapshot in time, not a measure of the threshold for staying. Customers who are satisfied with a product or service may still walk away if contact with the organization is structurally difficult. Loyalty is determined more by the sum of experiences than by one positive interaction.
Research from customer experience practice consistently shows that reducing negative experiences has a greater impact on loyalty than adding positive “wow moments.” Customers don’t appreciate exceptional service if basic interactions are already running smoothly. They simply expect it to work.
This explains why organizations with high CSAT scores still experience churn. Customers are satisfied with the product, but frustrated with the process. They have to wait, get redirected, have to repeat their data or receive conflicting information through different channels. That friction builds, and at some point the barrier to switching is low enough to make the move.
How does the link between customer effort and customer turnover work?
The link between customer effort and customer churn works through a threshold effect: any extra effort a customer has to make increases the likelihood that he or she will consider an alternative next time. High effort creates negative emotional associations with your brand, and those associations are strongly predictive of churn propensity.
Customers who have to make an effort to be helped are also more likely to tell others. Negative word of mouth as a result of high customer effort has a multiplier effect on your reputation. The damage is not limited to the customer who leaves, but also affects potential new customers.
What makes the relationship so strong is that high effort is almost always caused by avoidable obstacles. Think poor routing, fragmented systems, lack of channel continuity or employees who don’t have the right information. These are structural problems that you can fix, which also makes CES an actionable metric. Where CSAT tells you whether customers are happy, CES tells you where you need to improve the process.
In which contact moments is CES most predictive?
CES is most predictive in transactional contact moments where the customer has a specific goal, such as solving a problem, answering a question or making an inquiry. At those very moments, the effort the customer experiences is directly linked to whether he or she will return.
The most critical moments are:
- Complaint handling: Customers who file a complaint are already in a negative mood. If the process is then also laborious, the likelihood of churn is high.
- First contact with a new issue: The first time a customer calls or chats with an issue strongly determines how he or she rates the organization.
- Channel switching: When a customer has to switch from chat to phone or from email to face-to-face contact, the potential for high effort is high if there is no continuity.
- Self-service attempts that fail: Customers who first try to help themselves and then still have to call experience the combination of two failed attempts as doubly frustrating.
In relationship-related contact moments, such as an annual review meeting or a proactive update, CSAT is often more relevant. There, it is about the perception of the relationship as a whole, not the difficulty of a specific action.
Can CES and CSAT be used together?
Yes, CES and CSAT are complementary and together provide a more complete picture of the customer experience than each alone. CSAT provides insight into overall satisfaction and the emotional experience, while CES uncovers the operational friction that undermines loyalty. Together, they help you understand both how customers feel and why they leave.
An effective approach combines the two scores based on the type of contact moment. Use CES immediately after transactional interactions, such as a service call or complaint process. Use CSAT after relationship-related moments or if you want to measure overall brand perception. If necessary, add NPS (Net Promoter Score) to measure willingness to recommend.
The combination becomes really powerful when you link the scores to customer data over time. That way you can see which customers consistently give high effort scores, whether they correlate with lower satisfaction, and ultimately with churn. That gives you the steering information to proactively intervene before a customer leaves.
How do you reduce customer effort in daily contact practice?
Customer effort is reduced by structurally removing obstacles in the contact process. The most effective measures focus on routing, continuity and information availability, the three areas where most friction occurs in daily customer contact.
Practical steps that have an immediate effect:
- Improve routing: Get customers to the right person or department at first contact. Intelligent IVR systems and AI-driven call routing dramatically reduce call transfers.
- Give employees contextual information: If an employee already knows who is calling and what the reason is, the customer does not have to repeat their story. Link customer data to the contact channel.
- Ensure channel continuity: Customers who switch from chat to phone should not have to re-explain their context. Omnichannel integration makes this possible.
- Invest in self-service that really works: Good self-service lowers customer effort only if the answer is actually found. Unclear FAQs or a poorly searchable knowledge base do more harm than good.
- Measure CES by touchpoint: Only when you know where effort is highest can you make targeted improvements. Link CES to specific channels and moments in the customer journey.
How Pegamento helps reduce customer effort
We see daily how fragmented systems and poor routing unnecessarily increase customer effort. Organizations with multiple vendors for telephony, chat, e-mail and WhatsApp structurally struggle with channel blindness: employees lack context, customers have to repeat themselves, and managers can’t manage because there is no central overview.
Our approach focuses on removing that friction through smart combinations of proven modules, not costly customization, but targeted solutions that fit your situation. What we specifically offer:
- Intelligent call routing that brings customers directly to the right employee
- Omnichannel integration so that context travels with you from channel to channel
- AI-driven self-service that handles out-of-hours inquiries without customer effort
- Centralized reporting across all channels so you can measure and improve CES and other KPIs per touchpoint
- Agentic AI assistants that not only follow instructions but take initiative independently, an evolution from executive bots to self-thinking assistants that proactively support customer contact
Everything under one roof, from implementation to management and support, so you don’t have to deal with multiple vendors. Want to know where in your contact process the most customer effort is and how to address it? Check out our contact center solutions or contact us for a no-obligation consultation.
Frequently Asked Questions
Hoe vaak moet ik CES meten om betrouwbare data te verzamelen?
CES meet je het beste direct na een specifiek contactmoment, bij voorkeur binnen 24 uur nadat de interactie heeft plaatsgevonden. Hoe sneller de meting, hoe accurater de herinnering van de klant. Voor betrouwbare trends heb je minimaal 30 tot 50 responses per touchpoint nodig voordat je conclusies kunt trekken en verbeteringen kunt doorvoeren.
Wat is een goede CES-score en wanneer moet ik actie ondernemen?
Op een schaal van 1 tot 7 geldt een gemiddelde score van 5,5 of hoger doorgaans als goed, waarbij een lagere score meer moeite betekent. Maar absolute benchmarks zijn minder relevant dan je eigen trend over tijd en vergelijking per touchpoint. Actie is sowieso geboden als meer dan 20% van je klanten een score van 3 of lager geeft, of als je ziet dat de CES bij een specifiek kanaal structureel achterblijft bij de rest.
Kan ik CES ook inzetten voor B2B-klantrelaties, of is het vooral geschikt voor B2C?
CES werkt goed in zowel B2B als B2C, maar de toepassing verschilt. In B2B zijn er vaak meerdere contactpersonen per account en is de klantreis complexer, waardoor je CES het beste meet per specifiek contactpersoon en per type interactie. Let er in B2B ook op dat een hoge inspanning bij één contactpersoon de gehele accountrelatie kan beïnvloeden, waardoor de stakes per meting hoger zijn.
Wat zijn de meest gemaakte fouten bij het implementeren van CES-metingen?
De meest voorkomende fout is CES te breed inzetten, bijvoorbeeld als algemene tevredenheidsmeting aan het einde van een klantreis, in plaats van direct na een specifiek contactmoment. Andere valkuilen zijn: de score niet koppelen aan operationele data waardoor je niet weet wáár de frictie zit, en te lang wachten met de uitvraag waardoor de beleving al vervaagd is. Zorg ook dat je medewerkers begrijpen wat CES meet en hoe hun handelen direct invloed heeft op de score.
Hoe betrek ik mijn klantenservicemedewerkers bij het verbeteren van de CES?
Deel CES-resultaten actief met je team en koppel ze aan concrete situaties die medewerkers herkennen, zoals doorverbinden, herhaling van klantinformatie of lange wachttijden. Geef medewerkers inzicht in hun eigen scores per interactietype, zodat verbetering voelbaar en persoonlijk wordt. Betrek ze ook bij het identificeren van oorzaken: medewerkers weten vaak precies waar het proces vastloopt, maar krijgen zelden de ruimte om dat te melden.
Wat als mijn CES laag is maar mijn churn niet daalt — hoe interpreteer ik dat?
Een lage CES (veel moeite) die niet direct zichtbaar is in churndata kan twee dingen betekenen: de churn komt vertraagd, of er zijn andere factoren zoals contractverplichtingen of gebrek aan alternatieven die vertrek tijdelijk tegenhouden. Gebruik in dat geval CES als leading indicator en kijk of je een correlatie ziet tussen klanten met structureel lage CES-scores en latere opzeggingen. Die vertraging kan oplopen tot drie tot zes maanden, dus vroeg ingrijpen op basis van CES is altijd verstandiger dan wachten op zichtbare churn.
Hoe begin ik morgen met het meten van CES als mijn organisatie hier nog geen ervaring mee heeft?
Start klein: kies één contactmoment met veel volume en een hoog churnrisico, zoals klachtenafhandeling of eerste klantvragen, en stuur na afloop een korte CES-vraag via e-mail of SMS. Gebruik een eenvoudig zevenpuntsschaal met de vraag ‘Hoeveel moeite heeft u moeten doen om uw vraag opgelost te krijgen?’ en voeg optioneel een open tekstveld toe voor toelichting. Na vier tot zes weken heb je al genoeg data om de eerste patronen te zien en gerichte verbeteracties te prioriteren.
